EEG-IP: an international infant EEG data integration platform for the study of risk and resilience in autism and related conditions.

BACKGROUND: Establishing reliable predictive and diganostic biomarkers of autism would enhance early identification and facilitate targeted intervention during periods of greatest plasticity in early brain development. High impact research on biomarkers is currently limited by relatively small sample sizes and the complexity of the autism phenotype. METHODS: EEG-IP is an International Infant EEG Data Integration Platform developed to advance biomarker discovery by enhancing the large scale integration of multi-site data. Currently, this is the largest multi-site standardized dataset of infant EEG data. RESULTS: First, multi-site data from longitudinal cohort studies of infants at risk for autism was pooled in a common repository with 1382 EEG longitudinal recordings, linked behavioral data, from 432 infants between 3- to 36-months of age. Second, to address challenges of limited comparability across independent recordings, EEG-IP applied the Brain Imaging Data Structure (BIDS)-EEG standard, resulting in a harmonized, extendable, and integrated data state. Finally, the pooled and harmonized raw data was preprocessed using a common signal processing pipeline that maximizes signal isolation and minimizes data reduction. With EEG-IP, we produced a fully standardized data set, of the pooled, harmonized, and pre-processed EEG data from multiple sites. CONCLUSIONS: Implementing these integrated solutions for the first time with infant data has demonstrated success and challenges in generating a standardized multi-site data state. The challenges relate to annotation of signal sources, time, and ICA analysis during pre-processing. A number of future opportunities also emerge, including validation of analytic pipelines that can replicate existing findings and/or test novel hypotheses.

Cognitive control in the eye of the beholder: Electrocortical theta and alpha modulation during response preparation in a cued saccade task.

The oscillatory dynamics of medial frontal EEG theta and posterior alpha are implicated in the modulation of attention and cognitive control. We used a novel saccade cueing paradigm to examine whether theta and alpha are modulated by task difficulty during response preparation. After isolating and functionally classifying medial frontal and posterior alpha independent components, the EEG spectral power in these components was calculated on pro- and anti-saccade trials prior to response probes. The results of bootstrap re-sampling show that, compared to pro-saccade trials, correct anti-saccades are characterized by an increase in medial frontal theta and suppression of posterior alpha during the response preparation period. Furthermore, an absence of increased medial frontal theta prior to anti-saccades probes occurs on error trials, that is, a failure to control pre-potent eye movements. For these error trials, a burst in medial frontal theta is instead observed following error feedback. Our findings show that enhanced medial frontal theta is linked not only to dynamic cognitive control that is reactive (such as, after error commission), but is also an important prerequisite for success when behavioral control is challenged.

Watch out! Medial frontal cortex is activated by cues signaling potential changes in response demands.

The human medial frontal cortex and especially the anterior cingulate cortex (ACC) have been implicated in several aspects of performance monitoring. We examined event-related EEG during a general process of controlling attention by using a novel paradigm to elicit a medial frontal negativity (MFN) to stimuli that indicate potential changes in future response demands. Independent components analysis revealed that the latent factors that accounted for MFN activity to such changes also accounted for activity associated with the error-related negativity and the NoGo inhibitory N2. Given that the medial frontal activation to these changes varied reliably across subjects simply as a function of potential need to alter responses in the absence of error commission and response inhibition, we propose that the underlying basis for medial frontal activation in situations demanding ongoing monitoring of performance involves an increase in attention control, a factor common to all MFN paradigms.

Deconstructing the early visual electrocortical responses to face and house stimuli.

The initial timing of face-specific effects in event-related potentials (ERPs) is a point of contention in face-processing research. The occasional reports of a larger P100 to face stimuli compared to other image categories is often attributed to differences in low-level stimulus characteristics. Separating the P100 from the classic N170 effect has not been done except by adjusting stimuli to control for low-level stimulus characteristics, which yields robust face effects only after 130 ms. In the present study we use a stimulus set with minimal controls for low-level characteristics. This produces significantly larger (p < 0.01) P100 and N170 amplitudes for images of faces compared to houses in a group effect. However, with independent component analysis (ICA), we demonstrate that (a) the P100 scalp effect stems from a neural network that is indeed independent of that producing the N170 effect, despite the N170 component being active at the time of the P100; (b) compared to the N170 effect, the P100 effect is less reliable even when it is present because of intersubject variability; and (c) some individuals show a component with a larger response to houses over faces at the time of the P100 that is undetectable at the scalp because the activation of larger spatiotemporally overlapping activity cancels its field projection. Thus, with ICA, we are able to account for the general finding in the literature of a consistent N170 face effect and a less reliable P100 face effect at the level of anatomically independent electrocortical processes.

Inter-trial theta phase consistency during face processing in infants is associated with later emerging autism.

A growing body of research suggests that consistency in cortical activity may be a promising neurophysiological marker of autism spectrum disorder (ASD). In the current study we examined inter-trial coherence, a measure of phase consistency across trials, in the theta range (t-ITC: 3-6 Hz), as theta has been implicated in the processing of social and emotional stimuli in infants and adults. The sample included infants who had an older sibling with a confirmed ASD diagnosis and typically developing (TD) infants with no family history of ASD. The data were collected as part of the British Autism Study of Infant Siblings (BASIS) study. Infants between 6 and 10 months of age (Mage = 7.34, SDage = 1.21) performed a visual face processing task that included faces and scrambled, "face noise", stimuli. Follow-up assessments in higher likelihood infants were completed at 24 and again at 36 months to determine diagnostic outcomes. Analysis focused on posterior t-ITC during early (0-200 ms) and late (200-500 ms) visual processing stages commonly investigated in infant studies. t-ITC over posterior scalp regions during late stage face processing was significantly higher in TD and higher likelihood infants without ASD (HRA-), indicating reduced consistency in theta-band responses in higher likelihood infants who eventually receive a diagnosis of ASD (HRA+). These findings indicate that the temporal dynamics of theta during face processing relate to ASD outcomes. Reduced consistency of oscillatory dynamics at basic levels of infant sensory processing could have downstream effects on learning and social communication. LAY SUMMARY: We examined the consistency in brain responses to faces in infants at lower or higher familial likelihood for autism. Our results show that the consistency of EEG responses was lower during face processing in higher likelihood infants who eventually received a diagnosis of autism. These findings highlight that reduced consistency in brain activity during face processing in the first year of life is related to emerging autism.

Increased alpha suppression with age during involuntary memory retrieval.

Recent work suggests that while voluntary episodic memory declines with age, involuntary episodic memory, which comes to mind spontaneously without intention, remains relatively intact. However, the neurophysiology underlying these differences has yet to be established. The current study used electroencephalography (EEG) to investigate voluntary and involuntary retrieval in older and younger adults. Participants first encoded sounds, half of which were paired with pictures, the other half unpaired. EEG was then recorded as they listened to the sounds, with participants in the involuntary group performing a sound localization cover task, and those in the voluntary group additionally attempting to recall the associated pictures. Participants later reported which sounds brought the paired picture to mind during the localization task. Reaction times on the localization task were slower for voluntary than involuntary retrieval and for paired than unpaired sounds, possibly reflecting increased attentional demands of voluntary retrieval and interference from reactivation of the associated pictures respectively. For the EEG analyses, young adults showed greater alpha event-related desynchronization (ERD) during voluntary than involuntary retrieval at frontal and occipital sites, while older adults showed pronounced alpha ERD regardless of intention. Additionally, older adults showed greater ERD for paired than unpaired sounds at occipital sites, likely reflecting visual reactivation of the associated pictures. Young adults did not show this alpha ERD memory effect. Taken together, these data suggest that involuntary memory is largely preserved with age, but this may be due to older adults' greater recruitment of top-down control even when demand for such control is limited.

Use of Empirical Mode Decomposition in ERP Analysis to Classify Familial Risk and Diagnostic Outcomes for Autism Spectrum Disorder.

Event-related potentials (ERPs) activated by faces and gaze processing are found in individuals with autism spectrum disorder (ASD) in the early stages of their development and may serve as a putative biomarker to supplement behavioral diagnosis. We present a novel approach to the classification of visual ERPs collected from 6-month-old infants using intrinsic mode functions (IMFs) derived from empirical mode decomposition (EMD). Selected features were used as inputs to two machine learning methods (support vector machines and k-nearest neighbors (k-NN)) using nested cross validation. Different runs were executed for the modelling and classification of the participants in the control and high-risk (HR) groups and the classification of diagnosis outcome within the high-risk group: HR-ASD and HR-noASD. The highest accuracy in the classification of familial risk was 88.44%, achieved using a support vector machine (SVM). A maximum accuracy of 74.00% for classifying infants at risk who go on to develop ASD vs. those who do not was achieved through k-NN. IMF-based extracted features were highly effective in classifying infants by risk status, but less effective by diagnostic outcome. Advanced signal analysis of ERPs integrated with machine learning may be considered a first step toward the development of an early biomarker for ASD.

EEG Integrated Platform Lossless (EEG-IP-L) pre-processing pipeline for objective signal quality assessment incorporating data annotation and blind source separation.

BACKGROUND: The methods available for pre-processing EEG data are rapidly evolving as researchers gain access to vast computational resources; however, the field currently lacks a set of standardized approaches for data characterization, efficient interactive quality control review procedures, and large-scale automated processing that is compatible with High Performance Computing (HPC) resources. NEW METHOD: In this paper we describe an infrastructure for the development of standardized procedures for semi and fully automated pre-processing of EEG data. Our pipeline incorporates several methods to isolate cortical signal from noise, maintain maximal information from raw recordings and provide comprehensive quality control and data visualization. In addition, batch processing procedures are integrated to scale up analyses for processing hundreds or thousands of data sets using HPC clusters. RESULTS: We demonstrate here that by using the EEG Integrated Platform Lossless (EEG-IP-L) pipeline's signal quality annotations, significant increase in data retention is achieved when applying subsequent post-processing ERP segment rejection procedures. Further, we demonstrate that the increase in data retention does not attenuate the ERP signal. CONCLUSIONS: The EEG-IP-L state provides the infrastructure for an integrated platform that includes long-term data storage, minimal data manipulation and maximal signal retention, and flexibility in post processing strategies.

Association between spectral electroencephalography power and autism risk and diagnosis in early development.

Autism spectrum disorder (ASD) has its origins in the atypical development of brain networks. Infants who are at high familial risk for, and later diagnosed with ASD, show atypical activity in multiple electroencephalography (EEG) oscillatory measures. However, infant-sibling studies are often constrained by small sample sizes. We used the International Infant EEG Data Integration Platform, a multi-site dataset with 432 participants, including 222 at high-risk for ASD, from whom repeated measurements of EEG were collected between the ages of 3-36 months. We applied a latent growth curve model to test whether familial risk status predicts developmental trajectories of spectral power across the first 3 years of life, and whether these trajectories predict ASD outcome. Change in spectral EEG power in all frequency bands occurred during the first 3 years of life. Familial risk, but not a later diagnosis of ASD, was associated with reduced power at 3 months, and a steeper developmental change between 3 and 36 months in nearly all absolute power bands. ASD outcome was not associated with absolute power intercept or slope. No associations were found between risk or outcome and relative power. This study applied an analytic approach not used in previous prospective biomarker studies of ASD, which was modeled to reflect the temporal relationship between genetic susceptibility, brain development, and ASD diagnosis. Trajectories of spectral power appear to be predicted by familial risk; however, spectral power does not predict diagnostic outcome above and beyond familial risk status. Discrepancies between current results and previous studies are discussed. LAY SUMMARY: Infants with an older sibling who is diagnosed with ASD are at increased risk of developing ASD themselves. This article tested whether EEG spectral power in the first year of life can predict whether these infants did or did not develop ASD.

A wavelet based algorithm for the identification of oscillatory event-related potential components.

Event related potentials (ERPs) are very feeble alterations in the ongoing electroencephalogram (EEG) and their detection is a challenging problem. Based on the unique time-based parameters derived from wavelet coefficients and the asymmetry property of wavelets a novel algorithm to separate ERP components in single-trial EEG data is described. Though illustrated as a specific application to N170 ERP detection, the algorithm is a generalized approach that can be easily adapted to isolate different kinds of ERP components. The algorithm detected the N170 ERP component with a high level of accuracy. We demonstrate that the asymmetry method is more accurate than the matching wavelet algorithm and t-CWT method by 48.67 and 8.03 percent, respectively. This paper provides an off-line demonstration of the algorithm and considers issues related to the extension of the algorithm to real-time applications.

The error-related negativity associated with different strength of stimulus-response interference.

OBJECTIVE: The present study was aimed at clarifying the effect of stimulus-response compatibility (SRC) interference on the ERN. METHODS: We compared ERNs in two tasks differing in the level of interference, an arrow (AR) task classified as a Simon task and a more complex arrow-orientation (AO) task classified as a spatial-Stroop task. We also compared ERNs between partial errors (with initial incorrect movement corrected by a proper full response) and overt (uncorrected) errors. RESULTS: Behavioral response time and error rate indicated that both interference and ERN amplitude were larger for the AO task than for the AR task. There was no significant difference in the ERN amplitude between the partial and overt errors. CONCLUSIONS: The ERN becomes larger as a function of the SRC interference. SIGNIFICANCE: Our study presented evidence that the ERN may represent response-monitoring associated with the SRC interference.